code: def __init__(self, M_proj):
      super(ProjectLoss, self).__init__()
      img_w = 800
      img_h = 800
      M1 = np.array(
          [
              [img_w / 2, 0, 0, 0],
              [0, img_h / 2, 0, 0],
              [0, 0, 1, 0],
              [img_w / 2, img_h / 2, 0, 1],
          ]
      )
      M = M_proj @ M1
      M = M.astype(np.float32)
      self.register_buffer("M", torch.from_numpy(M))
  
      camera_matrix = M[:3, :3].T.copy()
      camera_matrix[0, 2] = 400
      camera_matrix[1, 2] = 400
      camera_matrix[2, 2] = 1
      intrinsics = np.array([camera_matrix]).astype(np.float64)
      self.register_buffer("intrinsics", torch.from_numpy(intrinsics))
  
      self.eps = 1e-5
      # self.projector = Reprojector(img_w,img_h,M_proj)
      # self.solver = PnPSolver(self.projector.M.numpy())
      # self.loss_fn = torch.nn.MSELoss(reduce=False, size_average=False)
      # self.loss_fn = torch.nn.MSELoss()
      self.loss_fn = torch.nn.L1Loss()
  
file_path: insightface/reconstruction/jmlr/losses.py
imports: import torch
  from torch import nn
  import torch.nn.functional
  import kornia
  import numpy
label_desc: Use image node_id 1 to create a new ProjectLoss object from inherited base classes: nn with example: obj = ProjectLoss(M_proj)